Figure 1

Antigen persists in discrete cell populations within the lymph node

  1. Experimental design
  2. Mean Ag-score is shown for each cell type for 2, 14, 21, and 42 days post vaccination.
  3. UMAP projection shows LEC subsets.
  4. UMAP projections show Ag-scores for LEC subsets for each timepoint.
  5. Mean Ag-score is shown for LEC subsets for each timepoint.
  6. Ag-scores are shown for each timepoint for each LEC subset.
  7. UMAP projection shows DC subsets.
  8. UMAP projections show Ag-scores for DC subsets for each timepoint.
  9. Mean Ag-score is shown for DC subsets for each timepoint.
  10. Ag-scores are shown for each timepoint for each DC subset.

Notes

  • Only experiment 1 is shown for the LEC 21 and 42 day timepoints (including for the UMAPs). Experiment 2 data include very few LECs for the 21 day timepoint.
  • Experiment 2 is shown for the DC 21 and 42 day timepoints. Experiment 1 data are lower quality (sparse signal, mostly zeros).
  • Subsets are only shown if there are >= 5 cells for each timepoint
  • CD45+ and CD45- cell types are only shown for the CD45+ and CD45- datasets, respectively.



Figure S1

  1. UMAP projections show cell types identified for CD45- and CD45+ samples.
  2. UMAP projections show LEC subsets identified for each timepoint.
  3. UMAP projections show DC subsets identified for each timepoint.
  4. The expression of key marker genes is shown for LEC subsets.
  5. The expression of key marker genes is shown for DC subsets.



Figure S2

  1. Ag-scores are shown for each timepoint for CD45- cell types. Two biological replicates are shown for the day 21 and day 42 timepoints. The number of cells identified for each cell type is shown above each timepoint.
  2. Ag-scores are shown for each timepoint for CD45+ cell types, as described in A.
  3. Ag-scores are shown for each timepoint for LEC subsets, as described in A.
  4. Ag-scores are shown for each timepoint for DC subsets, as described in A.



Figure 2

Identification of an antigen archiving gene signature

  1. Ag-score is shown for day 14 Ag-high and -low cells identified for LEC subsets with the highest Ag-score.
  2. The fraction of cells predicted to be Ag-competent is shown for each LEC subset 14, 21, and 42 days post vaccination.
  3. Ag-high module score is shown for Ag-low, Ag-high, and predicted Ag-competent LECs. The Spearman correlation between Ag classes and Ag-high module score is shown for each timepoint. One-sided p values were calculated and adjusted using Bonferroni correction.
  4. UMAP projections show cLEC Ag-high module scores for each timepoint.
  5. The expression of select genes from the Ag-high (top four) and Ag-low (bottom four) gene modules is shown for cLECs. Genes identified as top predictors for all three LEC subsets are shown.

Notes

  • Ag-low and -high cells were identified by separately clustering each d14 LEC subset for each sample into two groups based on Ag-score.
  • The cutoffs identified for the d14 dataset were then used to identify Ag-high cells for the other timepoints.
  • With this approach there is a different cutoff for each LEC subset, but the cutoff is consistent across timepoints.
## Called from: .f(.x[[i]], ...)
## debug at <text>#10: dat <- lec_so %>% subset(subtype == typ) %>% subset(tm %in% rf_tms) %>% 
##     subset(training_data == FALSE) %>% FetchData(c("pred_grp", 
##     "tm", .x)) %>% as_tibble(rownames = "cell_id") %>% pivot_longer(all_of(.x))
## Called from: .f(.x[[i]], ...)
## debug at <text>#10: dat <- lec_so %>% subset(subtype == typ) %>% subset(tm %in% rf_tms) %>% 
##     subset(training_data == FALSE) %>% FetchData(c("pred_grp", 
##     "tm", .x)) %>% as_tibble(rownames = "cell_id") %>% pivot_longer(all_of(.x))
## Called from: .f(.x[[i]], ...)
## debug at <text>#10: dat <- lec_so %>% subset(subtype == typ) %>% subset(tm %in% rf_tms) %>% 
##     subset(training_data == FALSE) %>% FetchData(c("pred_grp", 
##     "tm", .x)) %>% as_tibble(rownames = "cell_id") %>% pivot_longer(all_of(.x))
## Called from: .f(.x[[i]], ...)
## debug at <text>#10: dat <- lec_so %>% subset(subtype == typ) %>% subset(tm %in% rf_tms) %>% 
##     subset(training_data == FALSE) %>% FetchData(c("pred_grp", 
##     "tm", .x)) %>% as_tibble(rownames = "cell_id") %>% pivot_longer(all_of(.x))
## Called from: .f(.x[[i]], ...)
## debug at <text>#10: dat <- lec_so %>% subset(subtype == typ) %>% subset(tm %in% rf_tms) %>% 
##     subset(training_data == FALSE) %>% FetchData(c("pred_grp", 
##     "tm", .x)) %>% as_tibble(rownames = "cell_id") %>% pivot_longer(all_of(.x))
## Called from: .f(.x[[i]], ...)
## debug at <text>#10: dat <- lec_so %>% subset(subtype == typ) %>% subset(tm %in% rf_tms) %>% 
##     subset(training_data == FALSE) %>% FetchData(c("pred_grp", 
##     "tm", .x)) %>% as_tibble(rownames = "cell_id") %>% pivot_longer(all_of(.x))



Figure S3

  1. The fraction of cells predicted to be Ag-low and Ag-high is shown for each LEC subset.
  2. Ag-low module score is shown for Ag-low, Ag-high, and predicted Ag-competent LECs. The Spearman correlation between Ag classes and Ag-high module score is shown for each timepoint. One-sided p values were calculated and adjusted using Bonferroni correction.
  3. UMAP projections show fLEC Ag-high module scores for each timepoint.
  4. UMAP projections show collecting LEC Ag-high module scores for each timepoint.



Figure 3

Antigen archiving is enhanced by successive vaccinations

  1. Mean 21 day Ag-score is shown for LECs from mice that received a single vaccination (21 day or 42 day) or dual vaccination (21 day and 42 day).
  2. UMAP projections show 21 day Ag-scores for LEC subsets for single and dual vaccinations.
  3. Prior vaccination enhances antigen archiving. Ag-score is shown for single and dual vaccinations for the 21 day timepoint for each LEC subset. Other timepoints are shown in grey. P values were calculated using a one-sided Wilcoxon rank sum test with Bonferroni correction.
  4. Mean 42 day Ag-score is shown as described in A.
  5. UMAP projections show 42 day Ag-scores for LEC subsets as described in B.
  6. Successive vaccinations enhances retention of previously archived antigen. Ag-score is shown for the 42 day timepoint as described in C.
  7. 21 day and 42 day Ag-score is compared for LEC subsets.
  8. Ag-high module scores described in Figure 2 are shown for LECs that archived antigens from both vaccinations (double-high), from only one vaccination (single-high), or have low levels of both antigens (Ag-low). Module scores are shown for the corresponding LEC subset. P values were calculated using a one-sided Wilcoxon rank sum test with Bonferroni correction.
  9. Ag-low module scores are shown for LEC subset as described in H.



Figure S4

  1. Ag-score is shown for single and dual vaccinations for the 21 day timepoint for DC subsets from a biological replicate. This analysis was not performed for a biological replicate for LEC subsets due to the low number of LECs recovered (<40 total cells). Other timepoints are shown in grey. P values were calculated using a one-sided t test with Bonferroni correction.
  2. Ag-score is shown for the 42 day timepoint for LEC subsets from a biological replicate, as described in A.
  3. Ag-score is shown for the 42 day timepoint for DC subsets from a biological replicate, as described in A.
  4. 21 day and 42 day Ag-score is compared for LEC subsets from a biological replicate.



Figure 4

Antigen uptake by DCs is enhanced by successive vaccinations

  1. Mean 21 day Ag-score is shown for DCs from mice that received a single vaccination (21 day or 42 day) or dual vaccination (21 day and 42 day).
  2. Ag-score is shown for single and dual vaccinations for the 21 day timepoint for DC subsets with highest Ag-score. Other timepoints are shown in grey. P values were calculated using a one-sided Wilcoxon rank sum test with Bonferroni correction.
  3. Mean 42 day Ag-score is shown for DCs from mice that received single or dual vaccination as described in A.
  4. Ag-score is shown for single and dual vaccinations for the 42 day timepoint as described in B.

Notes

  • Need to add GeoMx figures showing Ag signal is highest in medula and sinus regions for both LECs and DCs.



Figure 5

Antigen archiving is impaired during CHIKV infection

  1. UMAP projections show LEC subsets for mock and CHIKV-infected mice.
  2. UMAP projections show predicted Ag-competent cLECs for mock and CHIKV-infected mice.
  3. The fraction of predicted Ag-competent cLECs is shown for mock and CHIKV-infected mice for each biological replicate.
  4. UMAP projections show cLEC Ag-high module scores for mock and CHIKV-infected mice.
  5. cLEC Ag-high module scores are shown for mock and CHIKV-infected mice for each biological replicate.
  6. Expression of cLEC Ag-high gene module is shown for mock and CHIKV-infected mice for top LEC subsets.

Notes

  • Show cLEC Ag-low results in main figure?



Figure S6

  1. The fraction of cells predicted to be Ag-competent is shown for mock and CHIKV-infected mice for each biological replicate.
  2. Ag-high and Ag-low module scores are shown for mock and CHIKV-infected mice for each biological replicate. P values were calculated using a two-sided Wilcoxon rank sum test with Bonferroni correction.



Session info

## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.6 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8          LC_NUMERIC=C                 
##  [3] LC_TIME=en_US.UTF-8           LC_COLLATE=en_US.UTF-8       
##  [5] LC_MONETARY=en_US.UTF-8       LC_MESSAGES=en_US.UTF-8      
##  [7] LC_PAPER=en_US.UTF-8          LC_NAME=en_US.UTF-8          
##  [9] LC_ADDRESS=en_US.UTF-8        LC_TELEPHONE=en_US.UTF-8     
## [11] LC_MEASUREMENT=en_US.UTF-8    LC_IDENTIFICATION=en_US.UTF-8
## 
## time zone: America/Denver
## tzcode source: system (glibc)
## 
## attached base packages:
##  [1] stats4    tools     grid      stats     graphics  grDevices utils    
##  [8] datasets  methods   base     
## 
## other attached packages:
##  [1] cli_3.6.1             ggtree_3.8.2          GOSemSim_2.26.1      
##  [4] org.Mm.eg.db_3.17.0   AnnotationDbi_1.62.2  IRanges_2.34.1       
##  [7] S4Vectors_0.38.2      Biobase_2.60.0        BiocGenerics_0.46.0  
## [10] msigdbr_7.5.1         enrichplot_1.20.3     clusterProfiler_4.8.3
## [13] caret_6.0-94          lattice_0.21-8        furrr_0.3.1          
## [16] future_1.33.0         ranger_0.15.1         rsample_1.2.0        
## [19] harmony_1.1.0         biomaRt_2.56.1        openxlsx_4.2.5.2     
## [22] MetBrewer_0.2.0       ggtext_0.1.2          ggtrace_0.2.0.9000   
## [25] qs_0.25.5             vroom_1.6.4           M3Drop_1.26.0        
## [28] numDeriv_2016.8-1.1   djvdj_0.1.0           gtools_3.9.4         
## [31] clustifyrdata_1.1.0   here_1.0.1            presto_1.0.0         
## [34] data.table_1.14.8     Rcpp_1.0.11           devtools_2.4.5       
## [37] usethis_2.2.2         ComplexHeatmap_2.16.0 patchwork_1.1.3      
## [40] scales_1.2.1          boot_1.3-28.1         clustifyr_1.12.0     
## [43] mixtools_2.0.0        broom_1.0.5           colorblindr_0.1.0    
## [46] colorspace_2.1-0      xlsx_0.6.5            RColorBrewer_1.1-3   
## [49] ggrepel_0.9.3         cowplot_1.1.1         knitr_1.44           
## [52] gprofiler2_0.2.2      SeuratObject_4.1.4    Seurat_4.4.0         
## [55] ggforce_0.4.1         ggbeeswarm_0.7.2      lubridate_1.9.3      
## [58] forcats_1.0.0         stringr_1.5.0         dplyr_1.1.3          
## [61] purrr_1.0.2           readr_2.1.4           tidyr_1.3.0          
## [64] tibble_3.2.1          ggplot2_3.4.4         tidyverse_2.0.0      
## 
## loaded via a namespace (and not attached):
##   [1] igraph_1.5.1                ica_1.0-3                  
##   [3] plotly_4.10.2               Formula_1.2-5              
##   [5] zlibbioc_1.46.0             tidyselect_1.2.0           
##   [7] bit_4.0.5                   doParallel_1.0.17          
##   [9] clue_0.3-65                 rjson_0.2.21               
##  [11] blob_1.2.4                  urlchecker_1.0.1           
##  [13] S4Arrays_1.0.6              parallel_4.3.1             
##  [15] png_0.1-8                   ggplotify_0.1.2            
##  [17] goftest_1.2-3               kernlab_0.9-32             
##  [19] densEstBayes_1.0-2.2        uwot_0.1.16                
##  [21] shadowtext_0.1.2            curl_5.1.0                 
##  [23] mime_0.12                   evaluate_0.22              
##  [25] tidytree_0.4.5              leiden_0.4.3               
##  [27] stringi_1.7.12              pROC_1.18.5                
##  [29] backports_1.4.1             XML_3.99-0.14              
##  [31] httpuv_1.6.11               magrittr_2.0.3             
##  [33] rappdirs_0.3.3              splines_4.3.1              
##  [35] prodlim_2023.08.28          RApiSerialize_0.1.2        
##  [37] ggraph_2.1.0                sctransform_0.4.0          
##  [39] sessioninfo_1.2.2           DBI_1.1.3                  
##  [41] jquerylib_0.1.4             withr_2.5.1                
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##  [45] lmtest_0.9-40               bdsmatrix_1.3-6            
##  [47] tidygraph_1.2.3             htmlwidgets_1.6.2          
##  [49] fs_1.6.3                    SingleCellExperiment_1.22.0
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##  [53] MatrixGenerics_1.12.3       reticulate_1.32.0          
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##  [57] timechange_0.2.0            foreach_1.5.2              
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##  [61] timeDate_4022.108           irlba_2.3.5.1              
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##  [65] lazyeval_0.2.2              yaml_2.3.7                 
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##  [75] codetools_0.2-19            base64enc_0.1-3            
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##  [87] GenomicRanges_1.52.1        aplot_0.2.2                
##  [89] spatstat.sparse_3.0-2       ape_5.7-1                  
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##  [97] pkgbuild_1.4.2              beeswarm_0.4.0             
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## [101] nlme_3.1-162                loo_2.6.0                  
## [103] HDO.db_0.99.1               dbplyr_2.3.4               
## [105] digest_0.6.33               Matrix_1.6-1.1             
## [107] farver_2.1.1                tzdb_0.4.0                 
## [109] reshape2_1.4.4              ModelMetrics_1.2.2.2       
## [111] yulab.utils_0.1.0           viridis_0.6.4              
## [113] rpart_4.1.19                glue_1.6.2                 
## [115] cachem_1.0.8                BiocFileCache_2.8.0        
## [117] polyclip_1.10-6             Hmisc_5.1-1                
## [119] generics_0.1.3              Biostrings_2.68.1          
## [121] mvtnorm_1.2-3               parallelly_1.36.0          
## [123] pkgload_1.3.3               statmod_1.5.0              
## [125] pbapply_1.7-2               SummarizedExperiment_1.30.2
## [127] gson_0.1.0                  utf8_1.2.3                 
## [129] gower_1.0.1                 graphlayouts_1.0.2         
## [131] StanHeaders_2.26.28         gridExtra_2.3              
## [133] shiny_1.7.5                 lava_1.7.3                 
## [135] GenomeInfoDbData_1.2.10     RCurl_1.98-1.12            
## [137] memoise_2.0.1               rmarkdown_2.25             
## [139] downloader_0.4              RANN_2.6.1                 
## [141] stringfish_0.15.8           spatstat.data_3.0-1        
## [143] rstudioapi_0.15.0           cluster_2.1.4              
## [145] QuickJSR_1.0.7              rstantools_2.3.1.1         
## [147] spatstat.utils_3.0-3        hms_1.1.3                  
## [149] fitdistrplus_1.1-11         munsell_0.5.0              
## [151] rlang_1.1.1                 GenomeInfoDb_1.36.4        
## [153] ipred_0.9-14                circlize_0.4.15            
## [155] mgcv_1.8-42                 xfun_0.40                  
## [157] remotes_2.4.2.1             recipes_1.0.8              
## [159] iterators_1.0.14            matrixStats_1.0.0          
## [161] reldist_1.7-2               abind_1.4-5                
## [163] rstan_2.32.3                treeio_1.24.3              
## [165] rJava_1.0-6                 bitops_1.0-7               
## [167] ps_1.7.5                    promises_1.2.1             
## [169] inline_0.3.19               scatterpie_0.2.1           
## [171] RSQLite_2.3.1               qvalue_2.32.0              
## [173] fgsea_1.26.0                DelayedArray_0.26.7        
## [175] GO.db_3.17.0                compiler_4.3.1             
## [177] prettyunits_1.2.0           listenv_0.9.0              
## [179] tensor_1.5                  MASS_7.3-60                
## [181] progress_1.2.2              BiocParallel_1.34.2        
## [183] gridtext_0.1.5              babelgene_22.9             
## [185] spatstat.random_3.1-6       R6_2.5.1                   
## [187] fastmap_1.1.1               fastmatch_1.1-4            
## [189] vipor_0.4.5                 ROCR_1.0-11                
## [191] nnet_7.3-19                 gtable_0.3.4               
## [193] KernSmooth_2.23-21          miniUI_0.1.1.1             
## [195] deldir_1.0-9                htmltools_0.5.6.1          
## [197] RcppParallel_5.1.7          bit64_4.0.5                
## [199] spatstat.explore_3.2-3      lifecycle_1.0.3            
## [201] zip_2.3.0                   processx_3.8.2             
## [203] callr_3.7.3                 xlsxjars_0.6.1             
## [205] sass_0.4.7                  vctrs_0.6.3                
## [207] spatstat.geom_3.2-5         DOSE_3.26.2                
## [209] ggfun_0.1.3                 sp_2.1-0                   
## [211] future.apply_1.11.0         entropy_1.3.1              
## [213] bslib_0.5.1                 pillar_1.9.0               
## [215] gplots_3.1.3                jsonlite_1.8.7             
## [217] GetoptLong_1.0.5